10 citations · 31 across the 5 of their papers we have counts for
5 papers · 1 filter
Automated Learning Rate Scheduler for Large-batch Training
Chiheon Kim, Saehoon Kim, Jongmin Kim +2
Large-batch training has been essential in leveraging large-scale datasets and models in deep learning. While it is computationally beneficial to use large batch sizes, it often re…
Hybrid Generative-Contrastive Representation Learning
Saehoon Kim, Sungwoong Kim, Juho Lee
Unsupervised representation learning has recently received lots of interest due to its powerful generalizability through effectively leveraging large-scale unlabeled data. There ar…
MxML: Mixture of Meta-Learners for Few-Shot Classification
Minseop Park, Jungtaek Kim, Saehoon Kim +2
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples. Most of…
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang +1
While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domai…
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Yanbin Liu, Juho Lee, Minseop Park +4
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-l…